I have written a number of posts on systemic thinking, which I believe is at the heart of Kanban Thinking. I’m currently referring to systemic thinking, rather than Systems Thinking, in order to try and avoid any confusion with any particular school of thought, of which Systems Thinking could be one. I have also written an number of posts on Cynefin. This post brings the two together.
Being able to start thinking systemically is not easy, because there are different types of system, and each requires a different approach to working within them. Dave Snowden has developed a framework known as Cynefin, which is a useful way of understanding some of the differences, and the implications of those differences.
Cynefin is a Welsh word, which translates as “place of our multiple affiliations”. Snowden describes it as a multi-ontological sense-making framework. The multi-ontological aspect refers to the fact that there are different types of system, all of which are may be valid for different contexts. The sense-making aspect refers to the exercise of gathering data before creating the framework, as opposed to categorisation where a framework is created first and onto which data is subsequently placed. In practice, this means that examples are first gathered first and placed relative to each other based on some criteria, and then boundaries are then drawn in order to understand the relationships. This allows the emergence of a framework that might not otherwise have been identified.
Cynefin has five domains; two ordered, two unordered, and one of disorder.
On the right hand side are the ordered domains simple and complicated. These are ordered because there is a direct relationship between cause and effect. Causality is known and clearly perceivable, predictable and repeatable for simple problems, or it is at least knowable, although less obvious due to separation in time and space, for complicated problems. As a result we can immediately get a sense of the situation first, before categorising or analysing, in or to decide how to respond with best or good practice.
On the left hand side are the unordered domains complex and chaotic. These are unordered because of the ambiguous relationship between cause and effect. Causality may be retrospectively coherent, but not repeatable for complex problems, or simply incoherent and not perceivable at all for chaotic problems. As a result we need to probe with experiments or act quickly and assertively before we can get a sense of the situation in order to know how to respond.
In the centre is the domain of disorder. This is where we are unsure what type of problem we are dealing with and as a result are likely to respond instinctively with our preferred approach rather than the one appropriate for the situation.
The Cynefin framework, and in particular its distinction between ordered and unordered systems, is useful in understanding two common ways of dealing with change.
On the ordered side, in the simple and complicated domains, we can take advantage of expert and common knowledge, using best and good practice, to define a desirable future state, and the work to close the gap from the current state.
This is the approach generally taken by managed change programs, which put together teams, define roadmaps and create backlogs of work to implement in order to complete the change. This is a perfectly valid approach when the system really is ordered, and not incompatible with Kanban Thinking. A future state Kanban System can be designed, with pre-determined work types and workflow, and an appropriate visualisation, WIP limits and other policies. Metrics can be gathered, but learning may be minimal because of the assumption that the correct solution can be known in advance. However, while short term results may be achieved, there is also the risk that the chosen practices being implemented are not appropriate, or get followed blindly and dogmatically, with the long term result being a fall over the cliff into chaos as already described.
On the unordered side, in the complex and chaotic domains, we need to be more experimental, using emergent and novel practice, to understand the current state and discover its evolutionary potential. The author John Kay describes this approach as being one of Obliquity in his book of the same name. Similarly, Tim Harford discusses the need for a variety of experiments, resilience to the possibility of failure of those experiments, and clear selection of which experiments to kill or continue in his book Adapt.
This is the approach that really harnesses the power of Kanban Thinking. A Kanban System can be designed to represent the current state with existing work types and work flow, an appropriate visualisation conservative WIP limits and other policies. Metrics can be used to further understand the current state and adjustments can be made to the work, its flow, visualisation and policies in the form of intentional experiments, run to learn how to evolve the system for greater impact. A more suitable system design is likely to emerge this way than could be envisaged up front, although there is the risk that the improvement could be achieved more quickly using expert guidance.
What becomes interesting with Cynefin is not the classification of whether something is simple, complicated, complex or chaotic, but how situations transition between the domains and across the boundaries. No domain is considered to better than the others because each is contextual. Moving from the complex to the complicated domain may be appropriate when optimising or exploiting a solution. Conversely, moving from the complicated to complex domain may be appropriate when wanting to innovate or explore an idea. Occasionally a short and shallow dive through chaos into complexity might be appropriate for more radical changes. A key transition to be aware of is the one from the simple to chaotic domain. This results from complacency and over reliance on best practice, which pushes problems over the cliff into chaos. This transition is known as a cliff, and represented differently by the squiggle, because recovery is a non-trivial and costly process.
Use of the domains can be further confusing because often it is discovered that scenarios may be in multiple domains at the same time because elements of it live in a different domains. Narrative fragments, in the form of short anecdotes, are a common and useful form for capturing the examples and identifying and understanding the differences. Taking coding as an example, someone could tell a story about learning a new language, which might be a simple problem with best practice about syntax and coding styles. At the same time, someone else could tell a story about using Test Driven Development with that language as a good practice for a more complicated challenge of detailed design and development. Yet another person could tell a story about using spikes to experiment with a complex feature which requires the emergence of an innovative new design and architecture. Finally, a further person could tell a story about dealing with the chaos resulting from having to react to defects found in some legacy spaghetti code.
I have found that having an understanding of these different system domains, and their dynamics, to be useful in guiding my approach to taking action when working to help organisations change and improve their system. While my experience may often lead me to believe I know the answer, there are times when there is no right answer, and sometime the answer is not even knowable. By helping others discover the answer for themselves, they are able to both get to a better place now, and be better positioned to be able to discover new answers for themselves in the future.